Prior work in few-shot learning: Existing approaches to few-shot learning can be loosely organized into the following two main-stream families. Optimization-based methods [1]}, [2]}, [3]} aim to learn a good parameter initialization for the classifier. These learned weights can then be quickly adapted to novel classes using gradient-based optimization on only a few labeled samples. Metric-based methods, on the other hand, aim to learn a task-independent embedding that can generalize to novel categories under a chosen distance metric, such as Euclidean distance [4]}, cosine distance [5]}, hyperbolic distance [6]}, or a distance parameterized by a neural network [7]}.
